Research Interest

My research leverages state-of-the-art computational methodologies, including density functional theory (DFT), classical and ab initio molecular dynamics (MD), chemoinformatics, and data-driven approaches, to elucidate reaction mechanisms and the fundamental origins of reactivity and selectivity. These insights enable the rational interpretation of experimental results and the model-guided design of efficient catalysts and engineered enzymes. A central goal of my research is to develop predictive, physically interpretable computational and machine learning (ML) models that accelerate discovery in catalysis and chemical biology. My work is highly interdisciplinary and tightly integrated with experimental collaborations to address challenges in chemistry and biochemistry.

Current research directions include:

• Theoretical investigations of reactivity and selectivity in organic and organometallic reactions.

• Computational catalyst design that integrates mechanistic descriptors with supervised and unsupervised ML models to uncover structure-reactivity relationships, predict reaction yields and selectivities, and guide the development of active catalysts.

• Multiscale simulations of enzymatic reaction mechanisms and selectivity.

• De novo enzyme design using integrated ML/AI frameworks for protein generation, structure prediction, and optimization of sequence, structure, and catalytic function (including RFdiffusion, AlphaFold, Rosetta, and Chai-1).

• Molecular dynamics simulations for protein dynamics, as well as protein-ligand and protein-protein binding free energy calculations.

TEACHING

• Computational Chemistry.

• Advanced - Physical Chemistry.

• Organic Structure and Selectivity.

Award and prize

• 2018 - 2019: Carl Tryggers Fellowship (CTS 17:198).

• 2016: Catalysis Science & Technology Poster Prize.